Detection and correction of robotic process automation failures

ABSTRACT

An example embodiment involves rules related to repairing software programs, wherein the rules associate indications of software program failures with repair applications that are configured to correct the software program failures. One or more processors are configured to: (i) receive, by a predictive model, a representation of an execution history of a particular software program, wherein the predictive model has been trained on a corpus of execution histories of the software programs; (ii) generate, by the predictive model and from the execution history, a failure prediction for the particular software program; (iii) receive, by an automated repair controller application, the failure prediction from the predictive model; (iv) based on applying the rules to the failure prediction, determine, by the automated repair controller application, a repair application from the repair applications; and (v) cause, by the automated repair controller application, the repair application to be executed within the network.

BACKGROUND

Robotic process automation (RPA) can be used within computing systems toautomate certain routine or repetitive tasks, such as scanning documentsfor keywords or phrases, sorting data into categories, moving files fromone location to another, obtaining information from or writinginformation to a server or database, generating analytics, and so on.The motivation for RPA is largely in its ability to offload mundane workfrom various individuals. In this way, these individuals can spend moretime on higher-level complex tasks that are more difficult or impossibleto automate. In some cases, RPA may involve a degree of artificialcognition (e.g., by employing machine learning models) in order to makepredictions or classifications.

Thus, enterprises and other organizations have been deploying software“bots” (e.g., programs, scripts, etc.) to carry out these tasks. It hasbeen observed, however, that bots often fail at a rate that is higherthan expected. This is frequently due to the bots being programmed basedon the assumption of a static environment. In practice, computingsystems and networks are dynamic, with data being moved about, servicesbeing modified, and devices being placed in and taken out of production.Consequently, the gain resulting from bots taking on the work done bysome individuals can be offset by the loss associated with having otherindividuals debug, correct, and otherwise manage the bots.

SUMMARY

The embodiments herein may overcome these and potentially othertechnical problems by providing predictions regarding the operation ofbots within a computing system or network. The predictions areautomatically generated, possibly based on trained machine models. Whena bot is predicted to be in a failure state or likely to fail, a set ofexpert system rules are applied to determine how to correct the bot'sbehavior. If the bot cannot be corrected, an alert is supplied to ahuman agent who can then investigate further and manually address theissue.

The embodiments may further involve maintaining records of bots(possibly in the form of configuration items) in a configurationmanagement database (CMDB). These records may be linked to recordsrepresenting software, devices, and services of the computing system ornetwork. In this manner, a human agent who is considering makingmodifications to such software, devices, and services is able todetermine the impacted bots. As such, the human agent can then determinewhether the changes can be made in a way that eliminates or mitigatesthis impact, or if the bots can be reconfigured to perform as expectedon the system as modified. Likewise, a human agent who is testing,debugging, or considering root cause of a bot failure may be able torapidly identify the software, devices, and services that the bot uses.Then, the human agent may be able to focus his or her analysis on thebot's interaction with these components.

Accordingly, a first example embodiment may involve persistent storagecontaining rules related to repairing software programs (bots) in anetwork, wherein the rules associate indications of software programfailures with repair applications that are configured to correctcorresponding software program failures. One or more processors may beconfigured to: (i) receive, by a predictive model, a representation ofan execution history of a particular software program of the softwareprograms, wherein the predictive model has been trained on a corpus ofexecution histories of the software programs in order to be able toestimate root causes of software program failures; (ii) generate, by thepredictive model and from the execution history, a failure predictionfor the particular software program, the failure prediction including anestimated root cause; (iii) receive, by an automated repair controllerapplication, the failure prediction from the predictive model; (iv)possibly based on applying the rules to the failure prediction,determine, by the automated repair controller application, a repairapplication from the repair applications that is configured to correctthe estimated root cause; and (v) cause, by the automated repaircontroller application, the repair application to be executed within thenetwork.

A second example embodiment may involve receiving, by a predictivemodel, a representation of an execution history of a particular softwareprogram (bot), wherein the predictive model has been trained on a corpusof execution histories of software programs in order to be able toestimate root causes of software program failures, wherein persistentstorage contains rules related to repairing software programs in anetwork, and wherein the rules associate indications of software programfailures with repair applications that are configured to correctcorresponding software program failures. The second example embodimentmay also involve generating, by the predictive model and from theexecution history, a failure prediction for the particular softwareprogram, the failure prediction including an estimated root cause. Thesecond example embodiment may also involve receiving, by an automatedrepair controller application, the failure prediction from thepredictive model. The second example embodiment may also involve,possibly based on applying the rules to the failure prediction,determining, by the automated repair controller application, a repairapplication from the repair applications that is configured to correctthe estimated root cause. The second example embodiment may also involvecausing, by the automated repair controller application, the repairapplication to be executed within the network.

A third example embodiment may involve persistent storage defining afirst configuration item representing an application deployed within anetwork, a second configuration item representing a software program(bot) that is deployable within the network, and a relationship betweenthe first configuration item and the second configuration item, whereinthe relationship indicates that the software program uses theapplication and that the application is used by the software program.One or more processors may be configured to: (i) receive an indicationthat a change has been applied to the application or has been arrangedto be applied to the application; (ii) possibly in response to receivingthe indication that the change has been applied to the application orhas been arranged to be applied to the application, identify therelationship between the first configuration item and the secondconfiguration item; (iii) possibly based on the relationship between thefirst configuration item and the second configuration item, determinethat the change can affect operation of the software program; and (iv)possibly in response to determining that the change can affect operationof the software program, provide a notification of the change to anagent associated with the software program.

A fourth example embodiment may involve receiving an indication that achange has been applied to an application deployed within a network orhas been arranged to be applied to the application, wherein persistentstorage defines a first configuration item representing the application,a second configuration item representing a software program (bot) thatis deployable within the network, and a relationship between the firstconfiguration item and the second configuration item, wherein therelationship indicates that the software program uses the applicationand that the application is used by the software program. The fourthexample embodiment may also involve, possibly in response to receivingthe indication that the change has been applied to the application orhas been arranged to be applied to the application, identifying therelationship between the first configuration item and the secondconfiguration item. The fourth example embodiment may also involve,possibly based on the relationship between the first configuration itemand the second configuration item, determining that the change canaffect operation of the software program. The fourth example embodimentmay also involve, possibly in response to determining that the changecan affect operation of the software program, providing a notificationof the change to an agent associated with the software program.

In a fifth example embodiment, an article of manufacture may include anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations in accordance with the first,second, third, and/or fourth example embodiment.

In a sixth example embodiment, a computing system may include at leastone processor, as well as memory and program instructions. The programinstructions may be stored in the memory, and upon execution by the atleast one processor, cause the computing system to perform operations inaccordance with the first, second, third, and/or fourth exampleembodiment.

In a seventh example embodiment, a system may include various means forcarrying out each of the operations of the first, second, third, and/orfourth example embodiment.

These, as well as other embodiments, aspects, advantages, andalternatives, will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic drawing of a computing device, inaccordance with example embodiments.

FIG. 2 illustrates a schematic drawing of a server device cluster, inaccordance with example embodiments.

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments.

FIG. 4 depicts a communication environment involving a remote networkmanagement architecture, in accordance with example embodiments.

FIG. 5A depicts another communication environment involving a remotenetwork management architecture, in accordance with example embodiments.

FIG. 5B is a flow chart, in accordance with example embodiments.

FIG. 6 depicts a remediation architecture, in accordance with exampleembodiments.

FIG. 7 depicts a log file, in accordance with example embodiments.

FIG. 8 depicts a set of domain knowledge rules, in accordance withexample embodiments.

FIG. 9 is a message flow diagram, in accordance with exampleembodiments.

FIG. 10 is a flow chart, in accordance with example embodiments.

FIG. 11 depicts a partial database schema, in accordance with exampleembodiments.

FIG. 12A depicts a visualization of configuration item relationships, inaccordance with example embodiments.

FIG. 12B depicts another visualization of configuration itemrelationships, in accordance with example embodiments.

FIG. 13A depicts a graphical user interface representing configurationitem relationships used in workflows, in accordance with exampleembodiments.

FIG. 13B depicts another graphical user interface representingconfiguration item relationships used in workflows, in accordance withexample embodiments.

FIG. 13C depicts workflows involving bots, in accordance with exampleembodiments.

FIG. 14 is a flow chart, in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

I. Introduction

A large enterprise is a complex entity with many interrelatedoperations. Some of these are found across the enterprise, such as humanresources (HR), supply chain, information technology (IT), and finance.However, each enterprise also has its own unique operations that provideessential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically useoff-the-shelf software applications, such as customer relationshipmanagement (CRM) and human capital management (HCM) packages. However,they may also need custom software applications to meet their own uniquerequirements. A large enterprise often has dozens or hundreds of thesecustom software applications. Nonetheless, the advantages provided bythe embodiments herein are not limited to large enterprises and may beapplicable to an enterprise, or any other type of organization, of anysize.

Many such software applications are developed by individual departmentswithin the enterprise. These range from simple spreadsheets tocustom-built software tools and databases. But the proliferation ofsiloed custom software applications has numerous disadvantages. Itnegatively impacts an enterprise's ability to run and grow itsoperations, innovate, and meet regulatory requirements. The enterprisemay find it difficult to integrate, streamline, and enhance itsoperations due to lack of a single system that unifies its subsystemsand data.

To efficiently create custom applications, enterprises would benefitfrom a remotely-hosted application platform that eliminates unnecessarydevelopment complexity. The goal of such a platform would be to reducetime-consuming, repetitive application development tasks so thatsoftware engineers and individuals in other roles can focus ondeveloping unique, high-value features.

In order to achieve this goal, the concept of Application Platform as aService (aPaaS) is introduced, to intelligently automate workflowsthroughout the enterprise. An aPaaS system is hosted remotely from theenterprise, but may access data, applications, and services within theenterprise by way of secure connections. Such an aPaaS system may have anumber of advantageous capabilities and characteristics. Theseadvantages and characteristics may be able to improve the enterprise'soperations and workflows for IT, HR, CRM, customer service, applicationdevelopment, and security.

The aPaaS system may support development and execution ofmodel-view-controller (MVC) applications. MVC applications divide theirfunctionality into three interconnected parts (model, view, andcontroller) in order to isolate representations of information from themanner in which the information is presented to the user, therebyallowing for efficient code reuse and parallel development. Theseapplications may be web-based, and offer create, read, update, anddelete (CRUD) capabilities. This allows new applications to be built ona common application infrastructure.

The aPaaS system may support standardized application components, suchas a standardized set of widgets for graphical user interface (GUI)development. In this way, applications built using the aPaaS system havea common look and feel. Other software components and modules may bestandardized as well. In some cases, this look and feel can be brandedor skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior ofapplications using metadata. This allows application behaviors to berapidly adapted to meet specific needs. Such an approach reducesdevelopment time and increases flexibility. Further, the aPaaS systemmay support GUI tools that facilitate metadata creation and management,thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces betweenapplications, so that software developers can avoid unwantedinter-application dependencies. Thus, the aPaaS system may implement aservice layer in which persistent state information and other data arestored.

The aPaaS system may support a rich set of integration features so thatthe applications thereon can interact with legacy applications andthird-party applications. For instance, the aPaaS system may support acustom employee-onboarding system that integrates with legacy HR, IT,and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore,since the aPaaS system may be remotely hosted, it should also utilizesecurity procedures when it interacts with systems in the enterprise orthird-party networks and services hosted outside of the enterprise. Forexample, the aPaaS system may be configured to share data amongst theenterprise and other parties to detect and identify common securitythreats.

Other features, functionality, and advantages of an aPaaS system mayexist. This description is for purpose of example and is not intended tobe limiting.

As an example of the aPaaS development process, a software developer maybe tasked to create a new application using the aPaaS system. First, thedeveloper may define the data model, which specifies the types of datathat the application uses and the relationships therebetween. Then, viaa GUI of the aPaaS system, the developer enters (e.g., uploads) the datamodel. The aPaaS system automatically creates all of the correspondingdatabase tables, fields, and relationships, which can then be accessedvia an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVCapplication with client-side interfaces and server-side CRUD logic. Thisgenerated application may serve as the basis of further development forthe user. Advantageously, the developer does not have to spend a largeamount of time on basic application functionality. Further, since theapplication may be web-based, it can be accessed from anyInternet-enabled client device. Alternatively or additionally, a localcopy of the application may be able to be accessed, for instance, whenInternet service is not available.

The aPaaS system may also support a rich set of pre-definedfunctionality that can be added to applications. These features includesupport for searching, email, templating, workflow design, reporting,analytics, social media, scripting, mobile-friendly output, andcustomized GUIs.

Such an aPaaS system may represent a GUI in various ways. For example, aserver device of the aPaaS system may generate a representation of a GUIusing a combination of HTML and JAVASCRIPT®. The JAVASCRIPT® may includeclient-side executable code, server-side executable code, or both. Theserver device may transmit or otherwise provide this representation to aclient device for the client device to display on a screen according toits locally-defined look and feel. Alternatively, a representation of aGUI may take other forms, such as an intermediate form (e.g., JAVA®byte-code) that a client device can use to directly generate graphicaloutput therefrom. Other possibilities exist.

Further, user interaction with GUI elements, such as buttons, menus,tabs, sliders, checkboxes, toggles, etc. may be referred to as“selection”, “activation”, or “actuation” thereof. These terms may beused regardless of whether the GUI elements are interacted with by wayof keyboard, pointing device, touchscreen, or another mechanism.

An aPaaS architecture is particularly powerful when integrated with anenterprise's network and used to manage such a network. The followingembodiments describe architectural and functional aspects of exampleaPaaS systems, as well as the features and advantages thereof.

II. Example Computing Devices and Cloud-Based Computing Environments

FIG. 1 is a simplified block diagram exemplifying a computing device100, illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 100 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. Some server devices may operate as clientdevices from time to time in order to perform particular operations, andsome client devices may incorporate server features.

In this example, computing device 100 includes processor 102, memory104, network interface 106, and input/output unit 108, all of which maybe coupled by system bus 110 or a similar mechanism. In someembodiments, computing device 100 may include other components and/orperipheral devices (e.g., detachable storage, printers, and so on).

Processor 102 may be one or more of any type of computer processingelement, such as a central processing unit (CPU), a co-processor (e.g.,a mathematics, graphics, or encryption co-processor), a digital signalprocessor (DSP), a network processor, and/or a form of integratedcircuit or controller that performs processor operations. In some cases,processor 102 may be one or more single-core processors. In other cases,processor 102 may be one or more multi-core processors with multipleindependent processing units. Processor 102 may also include registermemory for temporarily storing instructions being executed and relateddata, as well as cache memory for temporarily storing recently-usedinstructions and data.

Memory 104 may be any form of computer-usable memory, including but notlimited to random access memory (RAM), read-only memory (ROM), andnon-volatile memory (e.g., flash memory, hard disk drives, solid statedrives, compact discs (CDs), digital video discs (DVDs), and/or tapestorage). Thus, memory 104 represents both main memory units, as well aslong-term storage. Other types of memory may include biological memory.

Memory 104 may store program instructions and/or data on which programinstructions may operate. By way of example, memory 104 may store theseprogram instructions on a non-transitory, computer-readable medium, suchthat the instructions are executable by processor 102 to carry out anyof the methods, processes, or operations disclosed in this specificationor the accompanying drawings.

As shown in FIG. 1 , memory 104 may include firmware 104A, kernel 104B,and/or applications 104C. Firmware 104A may be program code used to bootor otherwise initiate some or all of computing device 100. Kernel 104Bmay be an operating system, including modules for memory management,scheduling and management of processes, input/output, and communication.Kernel 104B may also include device drivers that allow the operatingsystem to communicate with the hardware modules (e.g., memory units,networking interfaces, ports, and buses) of computing device 100.Applications 104C may be one or more user-space software programs, suchas web browsers or email clients, as well as any software libraries usedby these programs. Memory 104 may also store data used by these andother programs and applications.

Network interface 106 may take the form of one or more wirelineinterfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, andso on). Network interface 106 may also support communication over one ormore non-Ethernet media, such as coaxial cables or power lines, or overwide-area media, such as Synchronous Optical Networking (SONET) ordigital subscriber line (DSL) technologies. Network interface 106 mayadditionally take the form of one or more wireless interfaces, such asIEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or awide-area wireless interface. However, other forms of physical layerinterfaces and other types of standard or proprietary communicationprotocols may be used over network interface 106. Furthermore, networkinterface 106 may comprise multiple physical interfaces. For instance,some embodiments of computing device 100 may include Ethernet,BLUETOOTH®, and Wifi interfaces.

Input/output unit 108 may facilitate user and peripheral deviceinteraction with computing device 100. Input/output unit 108 may includeone or more types of input devices, such as a keyboard, a mouse, a touchscreen, and so on. Similarly, input/output unit 108 may include one ormore types of output devices, such as a screen, monitor, printer, and/orone or more light emitting diodes (LEDs). Additionally or alternatively,computing device 100 may communicate with other devices using auniversal serial bus (USB) or high-definition multimedia interface(HDMI) port interface, for example.

In some embodiments, one or more computing devices like computing device100 may be deployed to support an aPaaS architecture. The exact physicallocation, connectivity, and configuration of these computing devices maybe unknown and/or unimportant to client devices. Accordingly, thecomputing devices may be referred to as “cloud-based” devices that maybe housed at various remote data center locations.

FIG. 2 depicts a cloud-based server cluster 200 in accordance withexample embodiments. In FIG. 2 , operations of a computing device (e.g.,computing device 100) may be distributed between server devices 202,data storage 204, and routers 206, all of which may be connected bylocal cluster network 208. The number of server devices 202, datastorages 204, and routers 206 in server cluster 200 may depend on thecomputing task(s) and/or applications assigned to server cluster 200.

For example, server devices 202 can be configured to perform variouscomputing tasks of computing device 100. Thus, computing tasks can bedistributed among one or more of server devices 202. To the extent thatthese computing tasks can be performed in parallel, such a distributionof tasks may reduce the total time to complete these tasks and return aresult. For purposes of simplicity, both server cluster 200 andindividual server devices 202 may be referred to as a “server device.”This nomenclature should be understood to imply that one or moredistinct server devices, data storage devices, and cluster routers maybe involved in server device operations.

Data storage 204 may be data storage arrays that include drive arraycontrollers configured to manage read and write access to groups of harddisk drives and/or solid state drives. The drive array controllers,alone or in conjunction with server devices 202, may also be configuredto manage backup or redundant copies of the data stored in data storage204 to protect against drive failures or other types of failures thatprevent one or more of server devices 202 from accessing units of datastorage 204. Other types of memory aside from drives may be used.

Routers 206 may include networking equipment configured to provideinternal and external communications for server cluster 200. Forexample, routers 206 may include one or more packet-switching and/orrouting devices (including switches and/or gateways) configured toprovide (i) network communications between server devices 202 and datastorage 204 via local cluster network 208, and/or (ii) networkcommunications between server cluster 200 and other devices viacommunication link 210 to network 212.

Additionally, the configuration of routers 206 can be based at least inpart on the data communication requirements of server devices 202 anddata storage 204, the latency and throughput of the local clusternetwork 208, the latency, throughput, and cost of communication link210, and/or other factors that may contribute to the cost, speed,fault-tolerance, resiliency, efficiency, and/or other design goals ofthe system architecture.

As a possible example, data storage 204 may include any form ofdatabase, such as a structured query language (SQL) database. Varioustypes of data structures may store the information in such a database,including but not limited to tables, arrays, lists, trees, and tuples.Furthermore, any databases in data storage 204 may be monolithic ordistributed across multiple physical devices.

Server devices 202 may be configured to transmit data to and receivedata from data storage 204. This transmission and retrieval may take theform of SQL queries or other types of database queries, and the outputof such queries, respectively. Additional text, images, video, and/oraudio may be included as well. Furthermore, server devices 202 mayorganize the received data into web page or web applicationrepresentations. Such a representation may take the form of a markuplanguage, such as the hypertext markup language (HTML), the extensiblemarkup language (XML), or some other standardized or proprietary format.Moreover, server devices 202 may have the capability of executingvarious types of computerized scripting languages, such as but notlimited to Perl, Python, PHP Hypertext Preprocessor (PHP), Active ServerPages (ASP), JAVASCRIPT®, and so on. Computer program code written inthese languages may facilitate the providing of web pages to clientdevices, as well as client device interaction with the web pages.Alternatively or additionally, JAVA® may be used to facilitategeneration of web pages and/or to provide web application functionality.

III. Example Remote Network Management Architecture

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments. This architecture includes three maincomponents—managed network 300, remote network management platform 320,and public cloud networks 340—all connected by way of Internet 350.

A. Managed Networks

Managed network 300 may be, for example, an enterprise network used byan entity for computing and communications tasks, as well as storage ofdata. Thus, managed network 300 may include client devices 302, serverdevices 304, routers 306, virtual machines 308, firewall 310, and/orproxy servers 312. Client devices 302 may be embodied by computingdevice 100, server devices 304 may be embodied by computing device 100or server cluster 200, and routers 306 may be any type of router,switch, or gateway.

Virtual machines 308 may be embodied by one or more of computing device100 or server cluster 200. In general, a virtual machine is an emulationof a computing system, and mimics the functionality (e.g., processor,memory, and communication resources) of a physical computer. Onephysical computing system, such as server cluster 200, may support up tothousands of individual virtual machines. In some embodiments, virtualmachines 308 may be managed by a centralized server device orapplication that facilitates allocation of physical computing resourcesto individual virtual machines, as well as performance and errorreporting. Enterprises often employ virtual machines in order toallocate computing resources in an efficient, as needed fashion.Providers of virtualized computing systems include VMWARE® andMICROSOFT®.

Firewall 310 may be one or more specialized routers or server devicesthat protect managed network 300 from unauthorized attempts to accessthe devices, applications, and services therein, while allowingauthorized communication that is initiated from managed network 300.Firewall 310 may also provide intrusion detection, web filtering, virusscanning, application-layer gateways, and other applications orservices. In some embodiments not shown in FIG. 3 , managed network 300may include one or more virtual private network (VPN) gateways withwhich it communicates with remote network management platform 320 (seebelow).

Managed network 300 may also include one or more proxy servers 312. Anembodiment of proxy servers 312 may be a server application thatfacilitates communication and movement of data between managed network300, remote network management platform 320, and public cloud networks340. In particular, proxy servers 312 may be able to establish andmaintain secure communication sessions with one or more computationalinstances of remote network management platform 320. By way of such asession, remote network management platform 320 may be able to discoverand manage aspects of the architecture and configuration of managednetwork 300 and its components. Possibly with the assistance of proxyservers 312, remote network management platform 320 may also be able todiscover and manage aspects of public cloud networks 340 that are usedby managed network 300.

Firewalls, such as firewall 310, typically deny all communicationsessions that are incoming by way of Internet 350, unless such a sessionwas ultimately initiated from behind the firewall (i.e., from a deviceon managed network 300) or the firewall has been explicitly configuredto support the session. By placing proxy servers 312 behind firewall 310(e.g., within managed network 300 and protected by firewall 310), proxyservers 312 may be able to initiate these communication sessions throughfirewall 310. Thus, firewall 310 might not have to be specificallyconfigured to support incoming sessions from remote network managementplatform 320, thereby avoiding potential security risks to managednetwork 300.

In some cases, managed network 300 may consist of a few devices and asmall number of networks. In other deployments, managed network 300 mayspan multiple physical locations and include hundreds of networks andhundreds of thousands of devices. Thus, the architecture depicted inFIG. 3 is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity ofmanaged network 300, a varying number of proxy servers 312 may bedeployed therein. For example, each one of proxy servers 312 may beresponsible for communicating with remote network management platform320 regarding a portion of managed network 300. Alternatively oradditionally, sets of two or more proxy servers may be assigned to sucha portion of managed network 300 for purposes of load balancing,redundancy, and/or high availability.

B. Remote Network Management Platforms

Remote network management platform 320 is a hosted environment thatprovides aPaaS services to users, particularly to the operator ofmanaged network 300. These services may take the form of web-basedportals, for example, using the aforementioned web-based technologies.Thus, a user can securely access remote network management platform 320from, for example, client devices 302, or potentially from a clientdevice outside of managed network 300. By way of the web-based portals,users may design, test, and deploy applications, generate reports, viewanalytics, and perform other tasks. Remote network management platform320 may also be referred to as a multi-application platform.

As shown in FIG. 3 , remote network management platform 320 includesfour computational instances 322, 324, 326, and 328. Each of thesecomputational instances may represent one or more server nodes operatingdedicated copies of the aPaaS software and/or one or more databasenodes. The arrangement of server and database nodes on physical serverdevices and/or virtual machines can be flexible and may vary based onenterprise needs. In combination, these nodes may provide a set of webportals, services, and applications (e.g., a wholly-functioning aPaaSsystem) available to a particular enterprise. In some cases, a singleenterprise may use multiple computational instances.

For example, managed network 300 may be an enterprise customer of remotenetwork management platform 320, and may use computational instances322, 324, and 326. The reason for providing multiple computationalinstances to one customer is that the customer may wish to independentlydevelop, test, and deploy its applications and services. Thus,computational instance 322 may be dedicated to application developmentrelated to managed network 300, computational instance 324 may bededicated to testing these applications, and computational instance 326may be dedicated to the live operation of tested applications andservices. A computational instance may also be referred to as a hostedinstance, a remote instance, a customer instance, or by some otherdesignation. Any application deployed onto a computational instance maybe a scoped application, in that its access to databases within thecomputational instance can be restricted to certain elements therein(e.g., one or more particular database tables or particular rows withinone or more database tables).

For purposes of clarity, the disclosure herein refers to the arrangementof application nodes, database nodes, aPaaS software executing thereon,and underlying hardware as a “computational instance.” Note that usersmay colloquially refer to the graphical user interfaces provided therebyas “instances.” But unless it is defined otherwise herein, a“computational instance” is a computing system disposed within remotenetwork management platform 320.

The multi-instance architecture of remote network management platform320 is in contrast to conventional multi-tenant architectures, overwhich multi-instance architectures exhibit several advantages. Inmulti-tenant architectures, data from different customers (e.g.,enterprises) are comingled in a single database. While these customers'data are separate from one another, the separation is enforced by thesoftware that operates the single database. As a consequence, a securitybreach in this system may affect all customers' data, creatingadditional risk, especially for entities subject to governmental,healthcare, and/or financial regulation. Furthermore, any databaseoperations that affect one customer will likely affect all customerssharing that database. Thus, if there is an outage due to hardware orsoftware errors, this outage affects all such customers. Likewise, ifthe database is to be upgraded to meet the needs of one customer, itwill be unavailable to all customers during the upgrade process. Often,such maintenance windows will be long, due to the size of the shareddatabase.

In contrast, the multi-instance architecture provides each customer withits own database in a dedicated computing instance. This preventscomingling of customer data, and allows each instance to beindependently managed. For example, when one customer's instanceexperiences an outage due to errors or an upgrade, other computationalinstances are not impacted. Maintenance down time is limited because thedatabase only contains one customer's data. Further, the simpler designof the multi-instance architecture allows redundant copies of eachcustomer database and instance to be deployed in a geographicallydiverse fashion. This facilitates high availability, where the liveversion of the customer's instance can be moved when faults are detectedor maintenance is being performed.

In some embodiments, remote network management platform 320 may includeone or more central instances, controlled by the entity that operatesthis platform. Like a computational instance, a central instance mayinclude some number of application and database nodes disposed upon somenumber of physical server devices or virtual machines. Such a centralinstance may serve as a repository for specific configurations ofcomputational instances as well as data that can be shared amongst atleast some of the computational instances. For instance, definitions ofcommon security threats that could occur on the computational instances,software packages that are commonly discovered on the computationalinstances, and/or an application store for applications that can bedeployed to the computational instances may reside in a centralinstance. Computational instances may communicate with central instancesby way of well-defined interfaces in order to obtain this data.

In order to support multiple computational instances in an efficientfashion, remote network management platform 320 may implement aplurality of these instances on a single hardware platform. For example,when the aPaaS system is implemented on a server cluster such as servercluster 200, it may operate virtual machines that dedicate varyingamounts of computational, storage, and communication resources toinstances. But full virtualization of server cluster 200 might not benecessary, and other mechanisms may be used to separate instances. Insome examples, each instance may have a dedicated account and one ormore dedicated databases on server cluster 200. Alternatively, acomputational instance such as computational instance 322 may spanmultiple physical devices.

In some cases, a single server cluster of remote network managementplatform 320 may support multiple independent enterprises. Furthermore,as described below, remote network management platform 320 may includemultiple server clusters deployed in geographically diverse data centersin order to facilitate load balancing, redundancy, and/or highavailability.

C. Public Cloud Networks

Public cloud networks 340 may be remote server devices (e.g., aplurality of server clusters such as server cluster 200) that can beused for outsourced computation, data storage, communication, andservice hosting operations. These servers may be virtualized (i.e., theservers may be virtual machines). Examples of public cloud networks 340may include AMAZON WEB SERVICES® and MICROSOFT® AZURE®. Like remotenetwork management platform 320, multiple server clusters supportingpublic cloud networks 340 may be deployed at geographically diverselocations for purposes of load balancing, redundancy, and/or highavailability.

Managed network 300 may use one or more of public cloud networks 340 todeploy applications and services to its clients and customers. Forinstance, if managed network 300 provides online music streamingservices, public cloud networks 340 may store the music files andprovide web interface and streaming capabilities. In this way, theenterprise of managed network 300 does not have to build and maintainits own servers for these operations.

Remote network management platform 320 may include modules thatintegrate with public cloud networks 340 to expose virtual machines andmanaged services therein to managed network 300. The modules may allowusers to request virtual resources, discover allocated resources, andprovide flexible reporting for public cloud networks 340. In order toestablish this functionality, a user from managed network 300 mightfirst establish an account with public cloud networks 340, and request aset of associated resources. Then, the user may enter the accountinformation into the appropriate modules of remote network managementplatform 320. These modules may then automatically discover themanageable resources in the account, and also provide reports related tousage, performance, and billing.

D. Communication Support and Other Operations

Internet 350 may represent a portion of the global Internet. However,Internet 350 may alternatively represent a different type of network,such as a private wide-area or local-area packet-switched network.

FIG. 4 further illustrates the communication environment between managednetwork 300 and computational instance 322, and introduces additionalfeatures and alternative embodiments. In FIG. 4 , computational instance322 is replicated, in whole or in part, across data centers 400A and400B. These data centers may be geographically distant from one another,perhaps in different cities or different countries. Each data centerincludes support equipment that facilitates communication with managednetwork 300, as well as remote users.

In data center 400A, network traffic to and from external devices flowseither through VPN gateway 402A or firewall 404A. VPN gateway 402A maybe peered with VPN gateway 412 of managed network 300 by way of asecurity protocol such as Internet Protocol Security (IPSEC) orTransport Layer Security (TLS). Firewall 404A may be configured to allowaccess from authorized users, such as user 414 and remote user 416, andto deny access to unauthorized users. By way of firewall 404A, theseusers may access computational instance 322, and possibly othercomputational instances. Load balancer 406A may be used to distributetraffic amongst one or more physical or virtual server devices that hostcomputational instance 322. Load balancer 406A may simplify user accessby hiding the internal configuration of data center 400A, (e.g.,computational instance 322) from client devices. For instance, ifcomputational instance 322 includes multiple physical or virtualcomputing devices that share access to multiple databases, load balancer406A may distribute network traffic and processing tasks across thesecomputing devices and databases so that no one computing device ordatabase is significantly busier than the others. In some embodiments,computational instance 322 may include VPN gateway 402A, firewall 404A,and load balancer 406A.

Data center 400B may include its own versions of the components in datacenter 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer406B may perform the same or similar operations as VPN gateway 402A,firewall 404A, and load balancer 406A, respectively. Further, by way ofreal-time or near-real-time database replication and/or otheroperations, computational instance 322 may exist simultaneously in datacenters 400A and 400B.

Data centers 400A and 400B as shown in FIG. 4 may facilitate redundancyand high availability. In the configuration of FIG. 4 , data center 400Ais active and data center 400B is passive. Thus, data center 400A isserving all traffic to and from managed network 300, while the versionof computational instance 322 in data center 400B is being updated innear-real-time. Other configurations, such as one in which both datacenters are active, may be supported.

Should data center 400A fail in some fashion or otherwise becomeunavailable to users, data center 400B can take over as the active datacenter. For example, domain name system (DNS) servers that associate adomain name of computational instance 322 with one or more InternetProtocol (IP) addresses of data center 400A may re-associate the domainname with one or more IP addresses of data center 400B. After thisre-association completes (which may take less than one second or severalseconds), users may access computational instance 322 by way of datacenter 400B.

FIG. 4 also illustrates a possible configuration of managed network 300.As noted above, proxy servers 312 and user 414 may access computationalinstance 322 through firewall 310. Proxy servers 312 may also accessconfiguration items 410. In FIG. 4 , configuration items 410 may referto any or all of client devices 302, server devices 304, routers 306,and virtual machines 308, any applications or services executingthereon, as well as relationships between devices, applications, andservices. Thus, the term “configuration items” may be shorthand for anyphysical or virtual device, or any application or service remotelydiscoverable or managed by computational instance 322, or relationshipsbetween discovered devices, applications, and services. Configurationitems may be represented in a configuration management database (CMDB)of computational instance 322.

As noted above, VPN gateway 412 may provide a dedicated VPN to VPNgateway 402A. Such a VPN may be helpful when there is a significantamount of traffic between managed network 300 and computational instance322, or security policies otherwise suggest or require use of a VPNbetween these sites. In some embodiments, any device in managed network300 and/or computational instance 322 that directly communicates via theVPN is assigned a public IP address. Other devices in managed network300 and/or computational instance 322 may be assigned private IPaddresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255or 192.168.0.0-192.168.255.255 ranges, represented in shorthand assubnets 10.0.0.0/8 and 192.168.0.0/16, respectively).

IV. Example Device, Application, and Service Discovery

In order for remote network management platform 320 to administer thedevices, applications, and services of managed network 300, remotenetwork management platform 320 may first determine what devices arepresent in managed network 300, the configurations and operationalstatuses of these devices, and the applications and services provided bythe devices, as well as the relationships between discovered devices,applications, and services. As noted above, each device, application,service, and relationship may be referred to as a configuration item.The process of defining configuration items within managed network 300is referred to as discovery, and may be facilitated at least in part byproxy servers 312.

For purposes of the embodiments herein, an “application” may refer toone or more processes, threads, programs, client modules, servermodules, or any other software that executes on a device or group ofdevices. A “service” may refer to a high-level capability provided bymultiple applications executing on one or more devices working inconjunction with one another. For example, a high-level web service mayinvolve multiple web application server threads executing on one deviceand accessing information from a database application that executes onanother device.

FIG. 5A provides a logical depiction of how configuration items can bediscovered, as well as how information related to discoveredconfiguration items can be stored. For sake of simplicity, remotenetwork management platform 320, public cloud networks 340, and Internet350 are not shown.

In FIG. 5A, CMDB 500 and task list 502 are stored within computationalinstance 322. Computational instance 322 may transmit discovery commandsto proxy servers 312. In response, proxy servers 312 may transmit probesto various devices, applications, and services in managed network 300.These devices, applications, and services may transmit responses toproxy servers 312, and proxy servers 312 may then provide informationregarding discovered configuration items to CMDB 500 for storagetherein. Configuration items stored in CMDB 500 represent theenvironment of managed network 300.

Task list 502 represents a list of activities that proxy servers 312 areto perform on behalf of computational instance 322. As discovery takesplace, task list 502 is populated. Proxy servers 312 repeatedly querytask list 502, obtain the next task therein, and perform this task untiltask list 502 is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers 312 may be configured withinformation regarding one or more subnets in managed network 300 thatare reachable by way of proxy servers 312. For instance, proxy servers312 may be given the IP address range 192.168.0/24 as a subnet. Then,computational instance 322 may store this information in CMDB 500 andplace tasks in task list 502 for discovery of devices at each of theseaddresses.

FIG. 5A also depicts devices, applications, and services in managednetwork 300 as configuration items 504, 506, 508, 510, and 512. As notedabove, these configuration items represent a set of physical and/orvirtual devices (e.g., client devices, server devices, routers, orvirtual machines), applications executing thereon (e.g., web servers,email servers, databases, or storage arrays), relationshipstherebetween, as well as services that involve multiple individualconfiguration items.

Placing the tasks in task list 502 may trigger or otherwise cause proxyservers 312 to begin discovery. Alternatively or additionally, discoverymay be manually triggered or automatically triggered based on triggeringevents (e.g., discovery may automatically begin once per day at aparticular time).

In general, discovery may proceed in four logical phases: scanning,classification, identification, and exploration. Each phase of discoveryinvolves various types of probe messages being transmitted by proxyservers 312 to one or more devices in managed network 300. The responsesto these probes may be received and processed by proxy servers 312, andrepresentations thereof may be transmitted to CMDB 500. Thus, each phasecan result in more configuration items being discovered and stored inCMDB 500.

In the scanning phase, proxy servers 312 may probe each IP address inthe specified range of IP addresses for open Transmission ControlProtocol (TCP) and/or User Datagram Protocol (UDP) ports to determinethe general type of device. The presence of such open ports at an IPaddress may indicate that a particular application is operating on thedevice that is assigned the IP address, which in turn may identify theoperating system used by the device. For example, if TCP port 135 isopen, then the device is likely executing a WINDOWS® operating system.Similarly, if TCP port 22 is open, then the device is likely executing aUNIX® operating system, such as LINUX®. If UDP port 161 is open, thenthe device may be able to be further identified through the SimpleNetwork Management Protocol (SNMP). Other possibilities exist. Once thepresence of a device at a particular IP address and its open ports havebeen discovered, these configuration items are saved in CMDB 500.

In the classification phase, proxy servers 312 may further probe eachdiscovered device to determine the version of its operating system. Theprobes used for a particular device are based on information gatheredabout the devices during the scanning phase. For example, if a device isfound with TCP port 22 open, a set of UNIX®-specific probes may be used.Likewise, if a device is found with TCP port 135 open, a set ofWINDOWS®-specific probes may be used. For either case, an appropriateset of tasks may be placed in task list 502 for proxy servers 312 tocarry out. These tasks may result in proxy servers 312 logging on, orotherwise accessing information from the particular device. Forinstance, if TCP port 22 is open, proxy servers 312 may be instructed toinitiate a Secure Shell (SSH) connection to the particular device andobtain information about the operating system thereon from particularlocations in the file system. Based on this information, the operatingsystem may be determined. As an example, a UNIX® device with TCP port 22open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. Thisclassification information may be stored as one or more configurationitems in CMDB 500.

In the identification phase, proxy servers 312 may determine specificdetails about a classified device. The probes used during this phase maybe based on information gathered about the particular devices during theclassification phase. For example, if a device was classified as LINUX®,a set of LINUX®-specific probes may be used. Likewise, if a device wasclassified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probesmay be used. As was the case for the classification phase, anappropriate set of tasks may be placed in task list 502 for proxyservers 312 to carry out. These tasks may result in proxy servers 312reading information from the particular device, such as basicinput/output system (BIOS) information, serial numbers, networkinterface information, media access control address(es) assigned tothese network interface(s), IP address(es) used by the particular deviceand so on. This identification information may be stored as one or moreconfiguration items in CMDB 500.

In the exploration phase, proxy servers 312 may determine furtherdetails about the operational state of a classified device. The probesused during this phase may be based on information gathered about theparticular devices during the classification phase and/or theidentification phase. Again, an appropriate set of tasks may be placedin task list 502 for proxy servers 312 to carry out. These tasks mayresult in proxy servers 312 reading additional information from theparticular device, such as processor information, memory information,lists of running processes (applications), and so on. Once more, thediscovered information may be stored as one or more configuration itemsin CMDB 500.

Running discovery on a network device, such as a router, may utilizeSNMP. Instead of or in addition to determining a list of runningprocesses or other application-related information, discovery maydetermine additional subnets known to the router and the operationalstate of the router's network interfaces (e.g., active, inactive, queuelength, number of packets dropped, etc.). The IP addresses of theadditional subnets may be candidates for further discovery procedures.Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovereddevice, application, and service is available in CMDB 500. For example,after discovery, operating system version, hardware configuration, andnetwork configuration details for client devices, server devices, androuters in managed network 300, as well as applications executingthereon, may be stored. This collected information may be presented to auser in various ways to allow the user to view the hardware compositionand operational status of devices, as well as the characteristics ofservices that span multiple devices and applications.

Furthermore, CMDB 500 may include entries regarding dependencies andrelationships between configuration items. More specifically, anapplication that is executing on a particular server device, as well asthe services that rely on this application, may be represented as suchin CMDB 500. For example, suppose that a database application isexecuting on a server device, and that this database application is usedby a new employee onboarding service as well as a payroll service. Thus,if the server device is taken out of operation for maintenance, it isclear that the employee onboarding service and payroll service will beimpacted. Likewise, the dependencies and relationships betweenconfiguration items may be able to represent the services impacted whena particular router fails.

In general, dependencies and relationships between configuration itemsmay be displayed on a web-based interface and represented in ahierarchical fashion. Thus, adding, changing, or removing suchdependencies and relationships may be accomplished by way of thisinterface.

Furthermore, users from managed network 300 may develop workflows thatallow certain coordinated activities to take place across multiplediscovered devices. For instance, an IT workflow might allow the user tochange the common administrator password to all discovered LINUX®devices in a single operation.

In order for discovery to take place in the manner described above,proxy servers 312, CMDB 500, and/or one or more credential stores may beconfigured with credentials for one or more of the devices to bediscovered. Credentials may include any type of information needed inorder to access the devices. These may include userid/password pairs,certificates, and so on. In some embodiments, these credentials may bestored in encrypted fields of CMDB 500. Proxy servers 312 may containthe decryption key for the credentials so that proxy servers 312 can usethese credentials to log on to or otherwise access devices beingdiscovered.

The discovery process is depicted as a flow chart in FIG. 5B. At block520, the task list in the computational instance is populated, forinstance, with a range of IP addresses. At block 522, the scanning phasetakes place. Thus, the proxy servers probe the IP addresses for devicesusing these IP addresses, and attempt to determine the operating systemsthat are executing on these devices. At block 524, the classificationphase takes place. The proxy servers attempt to determine the operatingsystem version of the discovered devices. At block 526, theidentification phase takes place. The proxy servers attempt to determinethe hardware and/or software configuration of the discovered devices. Atblock 528, the exploration phase takes place. The proxy servers attemptto determine the operational state and applications executing on thediscovered devices. At block 530, further editing of the configurationitems representing the discovered devices and applications may takeplace. This editing may be automated and/or manual in nature.

The blocks represented in FIG. 5B are examples. Discovery may be ahighly configurable procedure that can have more or fewer phases, andthe operations of each phase may vary. In some cases, one or more phasesmay be customized, or may otherwise deviate from the exemplarydescriptions above.

In this manner, a remote network management platform may discover andinventory the hardware, software, and services deployed on and providedby the managed network. As noted above, this data may be stored in aCMDB of the associated computational instance as configuration items.For example, individual hardware components (e.g., computing devices,virtual servers, databases, routers, etc.) may be represented ashardware configuration items, while the applications installed and/orexecuting thereon may be represented as software configuration items.

The relationship between a software configuration item installed orexecuting on a hardware configuration item may take various forms, suchas “is hosted on”, “runs on”, or “depends on”. Thus, a databaseapplication installed on a server device may have the relationship “ishosted on” with the server device to indicate that the databaseapplication is hosted on the server device. In some embodiments, theserver device may have a reciprocal relationship of “used by” with thedatabase application to indicate that the server device is used by thedatabase application. These relationships may be automatically foundusing the discovery procedures described above, though it is possible tomanually set relationships as well.

The relationship between a service and one or more softwareconfiguration items may also take various forms. As an example, a webservice may include a web server software configuration item and adatabase application software configuration item, each installed ondifferent hardware configuration items. The web service may have a“depends on” relationship with both of these software configurationitems, while the software configuration items have a “used by”reciprocal relationship with the web service. Services might not be ableto be fully determined by discovery procedures, and instead may rely onservice mapping (e.g., probing configuration files and/or carrying outnetwork traffic analysis to determine service level relationshipsbetween configuration items) and possibly some extent of manualconfiguration.

Regardless of how relationship information is obtained, it can bevaluable for the operation of a managed network. Notably, IT personnelcan quickly determine where certain software applications are deployed,and what configuration items make up a service. This allows for rapidpinpointing of root causes of service outages or degradation. Forexample, if two different services are suffering from slow responsetimes, the CMDB can be queried (perhaps among other activities) todetermine that the root cause is a database application that is used byboth services having high processor utilization. Thus, IT personnel canaddress the database application rather than waste time considering thehealth and performance of other configuration items that make up theservices.

V. CMDB Identification Rules and Reconciliation

A CMDB, such as CMDB 500, provides a repository of configuration items,and when properly provisioned, can take on a key role in higher-layerapplications deployed within or involving a computational instance.These applications may relate to enterprise IT service management,operations management, asset management, configuration management,compliance, and so on.

For example, an IT service management application may use information inthe CMDB to determine applications and services that may be impacted bya component (e.g., a server device) that has malfunctioned, crashed, oris heavily loaded. Likewise, an asset management application may useinformation in the CMDB to determine which hardware and/or softwarecomponents are being used to support particular enterprise applications.As a consequence of the importance of the CMDB, it is desirable for theinformation stored therein to be accurate, consistent, and up to date.

A CMDB may be populated in various ways. As discussed above, a discoveryprocedure may automatically store information related to configurationitems in the CMDB. However, a CMDB can also be populated, as a whole orin part, by manual entry, configuration files, and third-party datasources. Given that multiple data sources may be able to update the CMDBat any time, it is possible that one data source may overwrite entriesof another data source. Also, two data sources may each create slightlydifferent entries for the same configuration item, resulting in a CMDBcontaining duplicate data. When either of these occurrences takes place,they can cause the health and utility of the CMDB to be reduced.

In order to mitigate this situation, these data sources might not writeconfiguration items directly to the CMDB. Instead, they may write to anidentification and reconciliation application programming interface(API). This API may use a set of configurable identification rules thatcan be used to uniquely identify configuration items and determinewhether and how they are written to the CMDB.

In general, an identification rule specifies a set of configuration itemattributes that can be used for this unique identification.Identification rules may also have priorities so that rules with higherpriorities are considered before rules with lower priorities.Additionally, a rule may be independent, in that the rule identifiesconfiguration items independently of other configuration items.Alternatively, the rule may be dependent, in that the rule first uses ametadata rule to identify a dependent configuration item.

Metadata rules describe which other configuration items are containedwithin a particular configuration item, or the host on which aparticular configuration item is deployed. For example, a networkdirectory service configuration item may contain a domain controllerconfiguration item, while a web server application configuration itemmay be hosted on a server device configuration item.

A goal of each identification rule is to use a combination of attributesthat can unambiguously distinguish a configuration item from all otherconfiguration items, and is expected not to change during the lifetimeof the configuration item. Some possible attributes for an exampleserver device may include serial number, location, operating system,operating system version, memory capacity, and so on. If a rulespecifies attributes that do not uniquely identify the configurationitem, then multiple components may be represented as the sameconfiguration item in the CMDB. Also, if a rule specifies attributesthat change for a particular configuration item, duplicate configurationitems may be created.

Thus, when a data source provides information regarding a configurationitem to the identification and reconciliation API, the API may attemptto match the information with one or more rules. If a match is found,the configuration item is written to the CMDB. If a match is not found,the configuration item may be held for further analysis.

Configuration item reconciliation procedures may be used to ensure thatonly authoritative data sources are allowed to overwrite configurationitem data in the CMDB. This reconciliation may also be rules-based. Forinstance, a reconciliation rule may specify that a particular datasource is authoritative for a particular configuration item type and setof attributes. Then, the identification and reconciliation API will onlypermit this authoritative data source to write to the particularconfiguration item, and writes from unauthorized data sources may beprevented. Thus, the authorized data source becomes the single source oftruth regarding the particular configuration item. In some cases, anunauthorized data source may be allowed to write to a configuration itemif it is creating the configuration item or the attributes to which itis writing are empty.

Additionally, multiple data sources may be authoritative for the sameconfiguration item or attributes thereof. To avoid ambiguities, thesedata sources may be assigned precedences that are taken into accountduring the writing of configuration items. For example, a secondaryauthorized data source may be able to write to a configuration item'sattribute until a primary authorized data source writes to thisattribute. Afterward, further writes to the attribute by the secondaryauthorized data source may be prevented.

In some cases, duplicate configuration items may be automaticallydetected by reconciliation procedures or in another fashion. Theseconfiguration items may be flagged for manual de-duplication.

VI. Robotic Process Automation Remediation

Robotic process automation (RPA) is a general term for the deploymentand use of software “bots” that automate human-computer interaction andcomputer-computer interaction. These bots may take the form of any typeof software, such as a compiled program, interpreted script,client-server application, and so on. Thus, bots may be referred to assoftware bots, software programs, or applications, for example.

The tasks that bots carry out may be simple, complex, or anywhere inbetween. Example tasks are candidates for RPA include data entry,scanning documents for keywords or phrases, sorting data intocategories, moving files from one location to another, obtaininginformation from or writing information to a server or database,generating analytics, troubleshooting, synchronizing data, collectingdata from multiple remote sources, and so on. It is possible for bot toperform a wide variety of additional tasks as well across manyfunctions, such as IT, HR, finance, engineering, customer service, justto name a few.

One of the advantages of RPA comes from the ability to automate many ofthe routine, error-prone, frequent, and manual tasks that humanstypically perform. This saves time and resources, allowing enterprisesto focus on more strategic efforts to propel high-level and/or complexinitiatives forward. Nonetheless, bots in real-world scenarios oftenfail because the computing systems and network on which they operate aredynamic in nature. Thus, the data, servers, services, interfaces, andother objects on which the bots rely may not be present where the botsare programmed to look, or may not exist at all. This means that theefficacy of a bot depends not only on the bot's programming but also theenvironment in which it operates. In some cases, bot failures may remainundetected for hours, days, or weeks, leaving important tasks notperformed.

As a result, while bots may offload some tasks that would otherwise beperformed by human agents, additional human agents may be required todebug, correct, and otherwise manage the bots. Without improvement tohow bots operate and are managed, the gains expected from bot deploymentwill be limited.

The embodiments herein address these and other technical problems withan architecture and techniques for remediation of bot failure. Such anarchitecture can be deployed within a computing system or network tomonitor the operation of bots, predict when particular bots have failedare likely to fail, and then take proactive measures to address thesefailures in a rapid fashion that is automated or semi-automated.

It has been observed that a large portion, perhaps 50% or more, of botfailures have a fairly limited number of root causes, such as improperauthentication credentials (e.g., wrong userid/password), an inabilityto access another device (e.g., a server is down or its address haschanged), or data expected to be in a particular location cannot befound. Thus, addressing even just a few of these scenarios canpotentially resolve the majority of real-world bot failures.

A. Remediation Architecture

FIG. 6 depicts an example remediation architecture 600. The softwarecomponents, data, and associated processing may take place on one ormore computing devices within a managed network and/or a remote networkmanagement platform, for example.

Bots 602 represent one or more bots as described above, configured tocarry out tasks on a computing system or network. Bots 602 may writeoutput to bot execution history 606, which could take the form of logfiles, entries in a database, or be arranged in some other manner. Anexample of bot execution history 606 in log form is shown in FIG. 7 andwill be discussed in more detail below.

Bot execution history 606 may provide logs to predictive model 608.These logs refer to any form of data that represents the output orexecution of bots 602. Thus, the logs may be a subset or processedversion of the output received by bot execution history 606.

Predictive model 608 may be a machine-learning, rules-based, or otherform of model that predicts whether the data in the logs represents botfailures. In the embodiments herein, supervised or unsupervised machinelearning models are assumed, but other techniques may be used togenerate predictions. Predictive model 608 may also be referred to as abot manager, in the sense that it can monitor and/or supervise theexecution of bots.

Regardless of type, predictive model 608 analyzes the logs and providesfailure predictions to automated repair controller 610. Such a failureprediction may identify the bot, the instance of execution of the botthat has experienced the predicted failure, a timestamp of the time ofthe failure, and/or a failure category, such as “authentication failed”,“file not found”, “server not responding” and so on. These failurepredictions may include estimates of the root causes for each determinedfailure.

Automated repair controller 610 applies domain knowledge 604 to thefailure predictions. If any automated repair procedures are determinedto be applicable, they are carried out. While the automated repairprocedures are represented as an arrow from automated repair controller610 to bots 602, such direct communication between these components neednot happen. While automated repair controller 610 could provideinformation to, reconfigure, and/or trigger the restart of one or moreof bots 604, automated repair controller 610 could alternatively oradditionally reconfigure, change, or restart one or more softwareprograms or hardware devices on the managed network. Further, automatedrepair controller 610 may look up and/or obtain data from a CMDB (suchas CMDB 500) in order to determine or carry out the automated repairprocedures.

In some embodiments, automated repair procedures may take the form ofone or more software programs or scripts that may operate on one or morecomputing devices of the managed network. For instance, automated repaircontroller 610 may cause, by way of a proxy server (e.g., proxy server312) for example, remote triggering the execution of a script thatreconfigures one or bots 602.

In the case that automated repair controller 610 cannot determine anautomated repair procedure for a particular failure (e.g., no suchprocedure is provided by domain knowledge 604), automated repaircontroller 610 may alert human agent 612. This alert may take the formof an email, phone call, text message, or push notification.Alternatively or additionally, the alert involve automated repaircontroller 610 opening an incident report in an incident trackingsystem, and the incident report being assigned to human agent 612.

Human agent 612 may be an administrator who has some degree of controlover the bots, the computing devices upon which they execute, and anyother computing devices accessed, relied on, or impacted by the bots.The alert may contain a representation of at least a relevant portion ofthe logs. From this information, human agent 612 may be able to repairthe bot and/or its operational environment.

In some embodiments, bots 602 may execute on a managed network (e.g.,managed network 300) and write bot execution history to a filesystem ordatabase on the managed network. Automated repair procedures may causeoperations to occur on the managed network. The remaining operations maytake place on a computational instance (e.g., computational instance322) of a remote network management platform (e.g., remote networkmanagement platform 320). Thus, predictive model 608, automated repaircontroller 610, and domain knowledge 604 may exist on the computationalinstance. The computational instance may obtain the logs by way of thediscovery procedures described above, or the managed network may pushthe logs to the computational instance, such as by way of the securefile transfer protocol. Human agent 612 may be an administrator of themanaged network with access to the computational instance (e.g., by wayof a web-based interface).

B. Predictive Analysis of Bot Execution History

FIG. 7 provides an example log file 700. This log file may represent theoutput generated by one or more of bots 602 (e.g., bot execution history606) and/or the logs provided to predictive model 608 (in someembodiments, the logs consist of a subset of the output). At least someof bots 604 may periodically or from time to time write their statusesto a log file. As just some examples, this status may indicate that abot is attempting to do something, has succeeded in doing something, hasfailed in doing something, or is reporting that it is idle. Log file 700may be dedicated to the bot, or may be a more generic log file shared byseveral bots or used multiple different types of applications executingon the computing system (e.g., a syslog file). Thus, the content andformat of log file 700 may vary between implementations.

In log file 700, one of bots 602 (rpa_bot1) is configured to log on to aservice executing on a computing device assigned the IP address 10.0.2.2every five minutes. When doing so, rpa_bot1 may retrieve data, writedata, change a setting or parameter, and/or carry out some otheroperation.

Each time that rpa_bot1 attempts to log on to the service, it recordsthe outcome of this attempt in log file 700. For example, at 15:53:29 onApr. 1, 2021, rpa_bot1 wrote the string “Thu, 1 Apr. 2021 15:53:29:rpa_bot1: auth success 10.0.2.2 ssh userid admin” to log file 700. Thefirst part of this string, “Thu, 1 Apr. 2021 15:53:29”, is a time stampindicating when the result of the attempt was known to rpa_bot1. After acolon delimiter, the next part of the string, “rpa_bot1”, indicates thename of the bot. After another colon delimiter, the final part of thestring, “auth success 10.0.2.2 ssh userid admin”, indicates thatrpa_bot1 was able to successfully log on and authenticate itself to theservice at 10.0.2.2 using SSH. Thus, this string represents an instanceof an authentication success.

In contrast, at 16:18:29 on Apr. 1, 2021, rpa_bot1 wrote the string“Thu, 1 Apr. 2021 16:18:29: rpa_bot1: auth failure 10.0.2.2 ssh useridadmin” to log file 700. Notably, the final part of the string, “authfailure 10.0.2.2 ssh userid admin”, indicates that rpa_bot1 was unableto successfully log on and authenticate itself to the service at10.0.2.2 using SSH. Thus, this string represents an instance of anauthentication failure. For example, the userid/password pair providedby rpa_bot1 as authentication credentials may have been incorrect.

Other types of failures could be represented in log file 700. Forexample, at 16:23:29 on Apr. 2, 2021, rpa_bot1 wrote the string “Thu, 2Apr. 2021 16:23:29: rpa_bot1: error—server unreachable 10.0.2.2” to logfile 700. This string indicates that rpa_bot1 was unable to access10.0.2.2 at all. For example, attempts to log on to the service at10.0.2.2 may have timed out. This may have resulted from the IP address10.0.2.2 being unreachable, perhaps because the service was moved to adifferent IP address.

Regardless, the text in log file 700 may be used by predictive model 608to classify each event. For example, predictive model 608 may be atrained machine learning model that can differentiate between strings inlog file 700 that represent failures from those that representsuccesses. In other words, predictive model 608 could classify stringscontaining the words “error” or “failure” as an indication of a failure,and also classify strings containing the words “success” as anindication of success. These indications may be represented as numbers,textually, or in a different manner. In other words, predictive model608 may be able to segment the data in log file 700 into successes andfailures, and then further classify the failures into types.

Thus, the types of error or the types of success may be reflected in theclassification. For instance, the failures in log file 700 that occurredon Apr. 1, 2021 may be classified as authentication failures, and thefailures in log file 700 that occurred on Apr. 2, 2021 may be classifiedas reachability failures. Other possibilities exist.

As such, predictive model 608 may operate in a supervised orunsupervised fashion. If predictive model 608 operates in a supervisedfashion, then it may be trained with a large number of log file entriesthat are manually labelled with their type of success or failure. Inthis way, predictive model 608 is able to learn patterns in log fileentries that are indicative of particular types of success or failure(e.g., one or more tokens or substrings of these entries). If predictivemodel 608 operates in an unsupervised fashion, then it may cluster logfile entries based on the content therein. This clustering may projectthese entries into an n-dimensional space based on some form of wordvector, paragraph vector, term frequency/inverse document frequency(TF/IDF), syntactic analysis, semantic analysis, or other techniques.Hybrid pre-trained models, such as Bidirectional Encoder Representationsfrom Transformers (BERT) could also be used.

Nonetheless, other non-learning classification models could be used. Forexample, a rules-based model may classify log file entries based onkeywords or keyphrases therein. Thus, the classification may be based onthe presence or absence of certain strings in each entry.

Further, in some embodiments, provided classifications may be eachassociated with a degree of confidence, such as a confidence value orconfidence interval. A confidence value may indicate the model'scalculated likelihood that the classification is correct (e.g., 70% or95%). A confidence interval may be a range of such values (e.g., 65%-75%or 93%-97%). These degrees of confidence indicate a signal strength forprediction. As such, they may be logged or stored by the model with arepresentation of the associated log file content so that a human usercan later determine why the model made a particular prediction.

C. Automated Repair

As shown in FIG. 6 , once entries of a log file, such as log file 700,are classified by predictive model 608, classifications representingfailure predictions may be provided to automated repair controller 610.These failure predictions may take the form of an integer, a string, orsome other type of representation. For example, and continuing with thecontent of FIG. 7 , a value of “1” may represent an authenticationfailure, a value of “2” may represent a reachability failure, and so on.In many practical scenarios, other information regarding such failuresmay be provided with a failure prediction, such as the name of the bot,the computing device on which the bot is executing, the computing devicethat the bot tried to access, and so on.

Automated repair controller 610 may incorporate aspects of domainknowledge 604 when determining how to address bot failures. To thatpoint, domain knowledge 604 may include or refer to instructions (e.g.,in the form of software programs and/or scripts) that may be able torepair bots and/or the environments in which they operate so thatfurther bot operations are more likely to succeed.

FIG. 8 provides an example of domain knowledge 604 in the form of table800. In this table, each entry contains an indication of failure type, adescription of the failure, a name of the bot subject to the failure, anIP address of a correspondent node with which the bot was attempting tocommunicate, and a reference to a repair script. In some embodiments,more or fewer fields may be present. Also, some fields may take onvarious forms. For example, the correspondent node address could besomething other than IP address (e.g., a domain name), and the repairscript field could be a URL, directory path, and/or file name of arepair script.

Automated repair controller 610 may search or iterate through domainknowledge 604 and apply one or more rules that match a failureprediction. This matching may involve text or parameters of the failurepredicting matching one or more fields of a record in domain knowledge604.

To this point, in table 800, record 802 is a rule that indicates afailure type of “1” and a description of “auth failure”. Thus, thefailure type is that of a bot that failed authentication. The bot nameis “rpa_bot1” and the correspondent node address is 10.0.2.2 (consistentwith log file 700). Thus, a matching failure prediction will contain thetext of or references to the failure type, bot name, and/orcorrespondent node address. Applying this rule to a matching failureprediction, may result in automated repair controller 610 causingexecution of a script to refresh the credentials of “rpa_bot1”. Forexample, current credentials may be centralized in a credential storewithin the managed network or computational instance, and these may beprovided to the bot.

Record 804 is a rule that indicates a failure type of “2” and adescription of “server unreachable”. Thus, the failure type is that of abot that was unable to communicate with a server. The bot name is“rpa_bot1” and the correspondent node address is 10.0.2.2 (againconsistent with log file 700). Thus, a matching failure prediction willcontain the text of or references to the failure type, bot name, and/orcorrespondent node address. Applying this rule to a matching failureprediction, may result in automated repair controller 610 causingexecution of a script to restart “rpa_bot1”, restart the server at10.0.2.2, or cause “rpa_bot1” to attempt the transaction with anothercorrespondent node address. For example, the service that “rpa_bot1” isattempting to access may have moved to a different IP address, and thisIP address may be provided to the bot.

The examples in FIGS. 7 and 8 are merely illustrative and other types offailures may be addressed by the embodiments herein. For instance,failures due to high memory or processor utilization (e.g., greater thanabout 90%) may be addressed by scheduling later execution of the bot andalerting a human agent. Likewise, failures due to a version mismatchbetween two units of software may be addressed by rolling back orupgrading the version of one of the units of software. Otherpossibilities exist.

In some embodiments, a “cold start” scenario can be supported. This typeof scenarios is particularly useful when a bot is being executed for thefirst time overall, or the first time after a modification to the bot orthe environment in which it executes (e.g., the managed network). Tothat end, before the bot is scheduled for operation, it can be testedwith a null transaction. The null transaction would test theconnectivity of the bot, and the liveness of the correspondent nodes(e.g., applications or services on other devices) with which itcommunicates. The bot and/or its correspondent nodes may need toexplicitly support such an operation. If the null transaction succeeds,then the bot is scheduled for operation. If the null transaction fails,an alert is provided to a human agent and the logs produced by the botor its correspondent nodes can be used to train predictive model 608.

D. Example Prediction and Repair Transaction

FIG. 9 depicts a prediction and repair transaction in accordance withexample embodiments. This transaction is just one possibility, and otherembodiments may exist. In FIG. 9 , bots 602, bot execution history 606,and proxy server 312 are disposed within managed network 300, whilepredictive model 608, automated repair controller 610, and domainknowledge 604 are disposed within computational instance 322. But insome embodiments, these components may be distributed in a differentfashion. For example, all of the components may be disposed in withinmanaged network 300 or within computational instance 322.

At step 900, bots 602 write their statuses to bot execution history 606,perhaps in the form of a log file. At step 902, a representation (e.g.,subset) of this status is provided to proxy server 312 (for example,proxy server 312 may retrieve the representation from bot executionhistory 606 based on a request from computational instance 322 orautomatically). At step 904, proxy server 312 may provide therepresentation to predictive model 608 (e.g., based on a request fromcomputational instance 322 or automatically).

At step 906, predictive model 608 may provide failure predictions toautomated repair controller 610. These failure predictions may be basedon processing of the representations by a machine learning model, forexample. At step 908, automated repair controller 610 may retrieverepair procedures from domain knowledge 604. In some embodiments, step908 may take place in response to step 906 or automatically at anearlier time. At step 910, automated repair controller 610 may transmitrepair procedures (directly or indirectly) to proxy server 312. At step912, proxy server 312 may cause the repair procedures to be carried out.This could involve restarting or reconfiguring bots 602, restarting orreconfiguring other software programs or computing devices of managednetwork 300, and/or some other activities.

E. Example Operations

FIG. 10 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 10 may be carried out by a computing device, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a computational instance of a remote network managementplatform or a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 10 may be simplified by the removal of any oneor more of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theother figures or otherwise described herein.

Block 1000 may involve receiving, by a predictive model, arepresentation of an execution history of a particular software program(bot), wherein the predictive model has been trained on a corpus ofexecution histories of software programs in order to be able to estimateroot causes of software program failures, wherein persistent storagecontains rules related to repairing software programs in a network, andwherein the rules associate indications of software program failureswith repair applications that are configured to correct correspondingsoftware program failures.

Block 1002 may involve generating, by the predictive model and from theexecution history, a failure prediction for the particular softwareprogram, the failure prediction including an estimated root cause.

Block 1004 may involve receiving, by an automated repair controllerapplication, the failure prediction from the predictive model.

Block 1006 may involve, possibly based on applying the rules to thefailure prediction, determining, by the automated repair controllerapplication, a repair application from the repair applications that isconfigured to correct the estimated root cause.

Block 1008 may involve causing, by the automated repair controllerapplication, the repair application to be executed within the network.

Some embodiments may further involve receiving, by the predictive model,a second representation of a second execution history of a secondparticular software program of the software programs; generating, by thepredictive model and from the second execution history, a second failureprediction for the second particular software program, the secondfailure prediction including a second estimated root cause; receiving,by the automated repair controller application, the second failureprediction from the predictive model; possibly based on applying therules to the second failure prediction, determine, by the automatedrepair controller application, that the rules do not specify a matchingrepair application; and causing, by the automated repair controllerapplication, an alert regarding the second failure prediction to betransmitted to a human agent.

Some embodiments may further involve, possibly after causing the alertto be transmitted to the human agent, receiving, from the human agent,an update to the rules that specifies the matching repair applicationfor the second failure prediction.

In some embodiments, the rules each include: (i) a repair applicationreference, and (ii) one or more of a software program name, a softwareprogram failure indication, or a network address.

In some embodiments, the failure prediction includes at least one of thesoftware program name, the software program failure indication, or thenetwork address.

In some embodiments, determining the repair application that isconfigured to correct the estimated root cause comprises selecting aparticular rule from the rules, wherein the particular rule matches oneor more of the software program name, the software program failureindication, or the network address included in the failure prediction,and wherein the particular rule contains a particular repair applicationreference to the repair application.

In some embodiments, the network address is of a computing device onwhich the particular software program executes or with which theparticular software program attempts to communicate.

In some embodiments, the estimated root cause is one or more of: missingauthentication credentials, or a server or application being unreachableor unresponsive.

In some embodiments, execution of the repair application causes theparticular software program to be restarted or reconfigured.

In some embodiments, execution of the repair application causes anapplication, device, or service used by the particular software programto be restarted or reconfigured.

In some embodiments, the execution history of the particular softwareprogram includes at least part of a log file generated by the particularsoftware program.

In some embodiments, the predictive model was either trained on thecorpus of execution histories using unsupervised machine learning, ortrained on labelled entries of the corpus of execution histories usingsupervised machine learning.

In some embodiments, the predictive model and the automated repaircontroller application are disposed within a computational instance of aremote network management platform that is configured to manage thenetwork, and wherein receiving the representation of the executionhistory and causing the repair application to be executed within thenetwork occur by way of a proxy server disposed within the network.

VII. Incorporation of Bots into CMDB Records

As noted above, a CMDB, such as CMDB 500, may contain a set of databasetables defining physical and virtual computing hardware, components,software, services, and other items deployed in a managed network, aswell as relationships therebetween. The content of the CMDB is intendedto represent the “ground truth” about these items, and may be regularlyupdated by discovery procedures and/or manually.

Given the importance of bots to numerous applications and services thatinvolve a managed network, it may be beneficial to integraterepresentations of bots into the CMDB as configuration items. This wouldallow associations to be made between: (i) a bot and the physical orvirtual device on which it executes, and/or (ii) the bot andcorrespondent nodes in the managed network with which the bot interacts.

When a bot fails, one of the most difficult problems is determiningwhether the root cause of the failure is due to an error in the botitself, its configuration, or one or more of the correspondent nodes. Bytracking relationships between the bots and other configuration items,the root cause analysis can be focused, resulting in faster and moreaccurate debugging and resolution procedures. Further, the existence ofthese relationships in the CMDB can be leveraged to provide automaticnotifications to human agents when the system may need attention, eitheron a proactive or reactive basis.

Current solutions merely involve the execution of bots, and are lackingthe level of integration and intelligent handling of bots describedherein. For example, human agents making changes to a managed networkmay not be aware that bots are automating mission-critical processes inthe network and may be impacted by the changes. This lack of visibilitymeans that these processes are more likely to fail due to planned orunplanned system maintenance.

The embodiments herein address these and other problems by makingrelationships between bots and what they rely on and/or impact explicit.As a consequence, the uptime and relatability of services provided by orto the managed network can be improved.

A. Example Database Schema

FIG. 11 provides an example database schema 1100 in accordance withpossible embodiments. The tables defined by such a schema would containrepresentations of digital and physical assets and services associatedwith a managed network. To that point, FIG. 11 provides just a portionof a full database schema that would reside in a CMDB. In alternativeembodiments, the tables and columns (attributes) of database schema 1100may have different names, store different values, and have more or lessinformation that what is presented herein.

Each item in database schema 1100 defines a database table and itsrelationships with other database tables in the schema. The core tableis configuration item (cmdb_ci) table 1102, which is arranged to storegeneral configuration items. Columns of table 1102 may includedefinitions of a category, class, description, DNS domain, IP address,MAC address, name, operational status, owner (human agent or group incharge of the configuration item), serial number, and sysid (uniqueidentifier) of the configuration item, if applicable.

Other tables may be defined as child tables that inherit the columns oftable 1102, while adding new columns of their own. Thus, table 1102 mayserve as the root of a hierarchy of tables that define configurationitems for hardware devices, software that operates on these devices, andso on. For example, and simplified for purposes of illustration,application (cmdb_ci_application) table 1106, process (cmdb_ci_process)table 1108, and other types of configuration item table 1104 may inheritfrom table 1102.

Table 1106 may define applications, which can be specified as acollection of files (some executable, some perhaps not executable) thatcontribute to or deliver a service. Types of applications may includedatabase applications, web applications, productivity applications, andso on. Table 1108 may define processes that are carried out by one ormore applications and/or as part of a service. For example, processesmay include onboarding of new employees, resetting of passwords,upgrading or rollback of software patches, and so on.

Like table 1102, other tables may be defined as child tables thatinherit the columns of table 1106, while adding new columns of theirown. Thus, table 1106 may serve as the root of a hierarchy of tablesthat define configuration items for various specific applications. Forexample, and simplified for purposes of illustration, digital runtime(cmdb_ci_digital runtime) table 1112, and other types of applicationtables 1110 may inherit from table 1106. Table 1112, in turn, may defineexecution environments (e.g., a JAVA® virtual machine) on which a botcan be executed.

Also like table 1102, other tables may be defined as child tables thatinherit the columns of table 1108, while adding new columns of theirown. Thus, table 1108 may serve as the root of a hierarchy of tablesthat define configuration items for various specific processes. Forexample, and simplified for purposes of illustration, bot process(cmdb_ci_botprocess) table 1114 may inherit from table 1108. Table 1114,in turn, may define bots that carry out processes.

Also not shown in FIG. 11 for purposes of simplicity, is a relationshiptable. Each entry in such a table may reference two configuration itemsdefined in the hierarchy of configuration item tables, as well as a typeof relationship between these items. Example relationships include “ishosted on”, “runs on”, “runs”, “depends on”, “used by”, and so on.

Example relationships are shown between table 1112 and table 1114. Theserelationships indicate that bots defined in table 1114 run on executionenvironments defined in table 1112, and execution environments definedin table 1112 run (execute) bots defined in table 1114. For example, anexecution environment may receive instructions from a computationalinstance containing information about a bot to execute as well asparameters to provide to the bot. Bots both may execute in suchexecution environments and then provide results of the execution to thecomputational instance.

Further relationships can be made between bots, their executionenvironments and various IT service management databases, such asincident databases, problem databases, knowledge databases, changerequest databases, as so on. These relationships may be made byreferring to the sysids of the bots and/or their execution environments.

B. Visualizing Relationships

One of the advantages of representing a bot as a configuration item anddefining its relationships with other configuration items is the abilityto rapidly determine these relationships. For example, various graphicaluser interface or command line tools may allow a human agent to specifya configuration item in a search interface, and search for bots thatinteract with or rely on that configuration item to some extent.Likewise, a human agent may specify a bot configuration item in a searchinterface and search for other configuration items that the botinteracts with or relies on to some extent. Alternatively, theserelationships may be automatically searched and determined when aconfiguration item (bot or otherwise) is viewed. In this manner, thehuman agent is able to determine the bots that might be impacted by achange to a configuration item, as well as the configuration items thatcould impact bot behavior, if applicable.

FIG. 12A provides an example visualization 1200. Visualization 1200could be a graphical user interface provided by a computational instanceof a remote network management platform. Visualization 1200 centersaround directory service configuration item 1208. This directory servicecould be MICROSOFT® ACTIVE DIRECTORY®, for example, or anotherLDAP-based, DNS-based, or alternative service.

As shown, three bots use this service, each represented by respectiveconfiguration items. Create user bot configuration item 1202 mayrepresent a bot that creates users in the directory service. Add user togroup bot configuration item 1204 may represent a bot that associatesusers in the directory service with groups of such users. Change userpermissions bot configuration item 1206 may represent a bot that changesthe permissions of a user in the directory service.

Visualization 1200 assists a human agent who is considering making achange to the directory service, such as a software upgrade, softwarerollback, change of address, change of configuration, or causing thedirectory service to be temporarily unavailable. This individual wouldunambiguously be able to identify that the bots associated withconfiguration items 1202, 1204, and 1206 are potentially impacted.Further, this individual would be able to contact the owners of thesebots to discuss the change before carrying it out. This may allow thoseowners to make corresponding changes to their respective bots inconjunction to the change made to the directory service. For example, ifthe change to the directory service is a change of its IP address ordomain name, each of the bots may be updated to use the new IP addressor domain name when accessing the directory service.

FIG. 12B provides an example visualization 1220. Visualization 1220 alsocould be a graphical user interface provided by a computational instanceof a remote network management platform. Visualization 1220 centersaround create user bot configuration item 1202. In accordance withvisualization 1200, the create user bot interacts with the directoryservice associated with directory service configuration item 1208. Butthis bot also interacts with a VPN service represented by VPN serviceconfiguration item 1222 and an HR database represented by HR databaseconfiguration item 1224. For example, the create user bot may beconfigured to add a new user to multiple systems (e.g., the directoryservice, the VPN service, and the HR database) when executed to onboardsuch a user.

Visualization 1220 assists a human agent determine the services withwhich the create user bot interacts. For instance, if the create userbot fails, the human agent may narrow his or her root cause analysis toconsidering problems with the create user bot itself, as well as thedirectory service, the VPN service, and the HR database. In this manner,the human agent knows which services could have possibly contributed tothe failure, or at least were most likely to do so. Further, if thehuman agent is testing a new version of the create user bot, he or sheis able to determine that this testing should include interactionsbetween the create user bot and each of the directory service, the VPNservice, and the HR database.

C. Integrating Bot Relationships into Workflows

In addition, other types of visualizations reflecting the relationshipsbetween these types of configuration items can be added to graphicaluser interfaces. For example, FIG. 13A depicts graphical user interface1300 for entering and/or viewing a change request. In enterprises,change requests are processes for the addition, modification, or removalof configuration items. The details of a change request, such as thereason of the change, the priority, the risk, the type of change, andthe change category are stored in a change request database.

Graphical user interface 1300 includes upper section 1302, specifyingthe change request, and lower section 1304, specifying relatedconfiguration items. In alternative embodiments, more or fewer sectionsmay be present.

Upper section 1302 displays attributes of the change request, along witheditable text boxes allowing the specification and/or modification ofeach of these attributes. For example, the number of the change requestis CHG0040007, it was requested by a “system admin” entity (e.g., asystem administrator), is of the “server reboot” category, and involvesthe configuration item “directory service”. In some embodiments, theconfiguration item may be specified by a unique number in addition to orrather than a text string. Further, upper section 1302 also specifiesthat the type of change request is “emergency”, its priority is“critical”, risk is “moderate” and impact is “high”. Put together, theseattributes indicate that the system administrator is requesting that thedirectory service server be rebooted with high priority (e.g., as soonas possible). As indicated by the short description attribute, the goalis to apply the latest software patches to the directory server.

The content of lower section 1304 may be pre-calculated or automaticallydetermined on the fly when graphical user interface 1300 is generated.Regardless, it indicates that the related configuration items are thecreate user, add user to group, and change user bots. Thus, the humanagent who is tasked with making this change is aware of the possibleimpact on these bots, and can notify their owners. The owners, in turn,may determine that they should test their bots against the patchedserver to verify that the bots are still compatible with the directoryservice.

FIG. 13B depicts graphical user interface 1310 for specifying and/orviewing a bot. Graphical user interface 1310 includes upper section1312, specifying the bot, and lower section 1314, specifying relatedconfiguration items. In alternative embodiments, more or fewer sectionsmay be present.

Upper section 1312 displays attributes of the bot, along with editabletext boxes allowing the specification and/or modification of each ofthese attributes. For example, the name of the bot is “create user”, itspriority is low, its timeout (the amount of time it waits before givingup on attempts to contact a server) is 5 minutes, it uses the executablescript “adcreateuser.exe” and its current stage is “draft”.

The content of lower section 1314 may be pre-calculated or automaticallydetermined on the fly when graphical user interface 1310 is generated.Regardless, it indicates that the related configuration items are thedigital runtime 5B, the VPN service, the directory service, and the HRdatabase. Thus, the human agent who is in charge of this bot is aware ofthe possible impact on that these components can have on the bot.

These embodiments facilitate a number of scenarios that improveapplication performance and/or avoid application down time. Some ofthese scenarios are illustrated in FIG. 13C.

In scenario 1320, a user creates an incident by way of an incidenttracking system of a computational instance. The incident may refer to atechnology-related problem that the user has experienced with themanaged network—particularly, the incident may relate to difficultiesthat the user has experienced logging into an application hosted on themanaged network or the computational instance. The computationalinstance may automatically search the CMDB for bots with relationshipsto the application. For any such relationships that are found, theowners of these bots are added to the watch list of the incident. Here,a watch list is a group of users that are notified when certain changesare made to the incident. This would allow the bot owners to be aware ofthe incident and quickly take appropriate measures (e.g., pausing theoperation of or reconfiguring their bots).

In scenario 1322, a bot automatically generates an incident related to afailure it experienced during an interaction with an application. Thefailure could be an authentication failure, a reachability, failure, orsome other operational failure. By way of this incident, the owner ofthe application is notified of the failure, and can rapidly work todetermine root cause. Without this automatic incident generation, theowner might not become aware of the failure for minutes or hours.

In scenario 1324, which was discussed above, a change request for anapplication is created. In response, a list of one or more potentiallyimpacted bots is provided prior to the change request being carried out.The implementer of the change request may then notify the owners ofthese bots. Alternatively or additionally, the creation of changerequest may automatically notify these owners. In this fashion, theowners can review the change request and take remedial action, ifnecessary, so that their bots continue to operate properly with theapplication.

D. Example Operations

FIG. 14 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 14 may be carried out by a computing device, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a computational instance of a remote network managementplatform or a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 14 may be simplified by the removal of any oneor more of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theother figures or otherwise described herein.

Block 1400 may involve receiving an indication that a change has beenapplied to an application deployed within a network or has been arrangedto be applied to the application, wherein persistent storage defines afirst configuration item representing the application, a secondconfiguration item representing a software program that is deployablewithin the network, and a relationship between the first configurationitem and the second configuration item, wherein the relationshipindicates that the software program uses the application and that theapplication is used by the software program.

Block 1402 may involve, possibly in response to receiving the indicationthat the change has been applied to the application or has been arrangedto be applied to the application, identifying the relationship betweenthe first configuration item and the second configuration item.

Block 1404 may involve, based on the relationship between the firstconfiguration item and the second configuration item, determining thatthe change can affect operation of the software program.

Block 1406 may involve, in response to determining that the change canaffect operation of the software program, providing a notification ofthe change to an agent associated with the software program.

Some embodiments may involve the persistent storage also defining athird configuration item representing a second software program that isdeployable within the network, and a second relationship between thefirst configuration item and the third configuration item, wherein therelationship indicates that the second software program uses theapplication and that the application is used by the second softwareprogram. These embodiments may also involve: (i) possibly in response toreceiving the indication that the change has been applied to theapplication or has been arranged to be applied to the application,identifying the second relationship between the first configuration itemand the third configuration item; (ii) possibly based on the secondrelationship between the first configuration item and the thirdconfiguration item, determining that the change can affect operation ofthe second software program; and (iii) possibly in response todetermining that the change can affect operation of the second softwareprogram, providing a second notification of the change to a second agentassociated with the second software program.

Some embodiments may involve the persistent storage also defining athird configuration item representing a second application deployedwithin the network, and a second relationship between the secondconfiguration item and the third configuration item, wherein the secondrelationship indicates that the software program uses the secondapplication and that the second application is used by the softwareprogram. These embodiments may further involve: (i) receiving a secondindication that a second change has been applied to the secondapplication or has been arranged to be applied to the secondapplication; (ii) possibly in response to receiving the secondindication that the second change has been applied to the secondapplication or has been arranged to be applied to the secondapplication, identifying the second relationship between the secondconfiguration item and the third configuration item; (iii) possiblybased on the second relationship between the second configuration itemand the third configuration item, determining that the second change canaffect operation of the software program; and (iv) possibly in responseto determining that the second change can affect operation of thesoftware program, providing a second notification of the second changeto the agent associated with the software program.

Some embodiments may involve the persistent storage also definingrecords of incidents related to the network. These embodiments may alsoinvolve: (i) receiving a new record of an incident related to thesoftware program experiencing a failure when attempting to use theapplication; and (ii) possibly based on the relationship between thefirst configuration item and the second configuration item, providing asecond notification of the new record to a second agent associated withthe application.

Some embodiments may involve the persistent storage also definingrecords of incidents related to the network. These embodiments may alsoinvolve: (i) receiving a new record of an incident related to a failurewhen attempting to use the application; and (ii) possibly based on therelationship between the first configuration item and the secondconfiguration item, providing a second notification of the new record tothe agent associated with the software program.

In some embodiments, the first configuration item is stored in a firstconfiguration item table of a database, wherein the second configurationitem is stored in a second configuration item table of the database,wherein the relationship is stored in a relationship table of thedatabase, wherein receiving the indication that the change has beenapplied to the application or has been arranged to be applied to theapplication comprises searching the first configuration item table forthe first configuration item, wherein identifying the relationshipbetween the first configuration item and the second configuration itemcomprises searching the relationship table for relationships involvingthe first configuration item, and wherein providing the notification ofthe change to the agent associated with the software program comprisessearching the second configuration item table for the secondconfiguration item.

In some embodiments, the persistent storage also defines records ofchange requests related to the network, wherein receiving the indicationthat the change has been applied to the application or has been arrangedto be applied to the application comprises receiving a change requestthat references the first configuration item.

In some embodiments, the persistent storage also defines a thirdconfiguration item representing an execution environment, and a secondrelationship between the second configuration item and the thirdconfiguration item, wherein the second relationship indicates that thesoftware program executes within the execution environment.

In some embodiments, determining that the change can effect operation ofthe software program comprises determining that the change is related toan address of the application, an upgrade of the application, or arollback of the application.

In some embodiments, providing the notification of the change to theagent associated with the software program comprises transmitting anemail, text message, voice call, application-specific message, orweb-based message to the agent.

Some embodiments may further involve generating and transmitting, to aclient device, a visual representation of a graph, wherein the visualrepresentation includes the first configuration item and the secondconfiguration item as nodes of the graph and the relationship as an edgebetween the nodes.

VIII. Closing

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid-state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong-term storage, like ROM, optical or magnetic disks, solid-statedrives, or compact disc read only memory (CD-ROM), for example. Thecomputer readable media can also be any other volatile or non-volatilestorage systems. A computer readable medium can be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments could includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A system comprising: persistent storagecontaining rules related to repairing software programs in a network,wherein the rules associate indications of software program failureswith repair applications implemented by an automated repair controlleraccording to domain knowledge of the network, wherein the repairapplications are configured to correct corresponding software programfailures; and one or more processors configured to: receive, by apredictive model, a representation of an execution history of aparticular software program of the software programs, wherein thepredictive model has been trained on a corpus of execution histories ofthe software programs in order to be able to estimate root causes ofsoftware program failures; generate, by the predictive model and fromthe execution history, a failure prediction for the particular softwareprogram, the failure prediction including an estimated root cause;receive, by an automated repair controller application, the failureprediction from the predictive model; based on applying the rules to thefailure prediction, determine, by the automated repair controllerapplication, a repair application from the repair applications that isconfigured to correct the estimated root cause; and cause, by theautomated repair controller application, the repair application to beexecuted within the network.
 2. The system of claim 1, wherein the oneor more processors are further configured to: receive, by the predictivemodel, a second representation of a second execution history of a secondparticular software program of the software programs; generate, by thepredictive model and from the second execution history, a second failureprediction for the second particular software program, the secondfailure prediction including a second estimated root cause; receive, bythe automated repair controller application, the second failureprediction from the predictive model; based on applying the rules to thesecond failure prediction, determine, by the automated repair controllerapplication, that the rules do not specify a matching repairapplication; and cause, by the automated repair controller application,an alert regarding the second failure prediction to be transmitted to ahuman agent.
 3. The system of claim 2, wherein the one or moreprocessors are further configured to: after causing the alert to betransmitted to the human agent, receiving, from the human agent, anupdate to the rules that specifies the matching repair application forthe second failure prediction.
 4. The system of claim 1, wherein therules each include: (i) a repair application reference, and (ii) one ormore of a software program name, a software program failure indication,or a network address.
 5. The system of claim 4, wherein the failureprediction includes at least one of the software program name, thesoftware program failure indication, or the network address.
 6. Thesystem of claim 5, wherein determining the repair application that isconfigured to correct the estimated root cause comprises: selecting aparticular rule from the rules, wherein the particular rule matches oneor more of the software program name, the software program failureindication, or the network address included in the failure prediction,and wherein the particular rule contains a particular repair applicationreference to the repair application.
 7. The system of claim 4, whereinthe network address is of a computing device on which the particularsoftware program executes or with which the particular software programattempts to communicate.
 8. The system of claim 1, wherein the estimatedroot cause is one or more of: missing authentication credentials, or aserver or application being unreachable or unresponsive.
 9. The systemof claim 1, wherein execution of the repair application causes theparticular software program to be restarted or reconfigured.
 10. Thesystem of claim 1, wherein execution of the repair application causes anapplication, device, or service used by the particular software programto be restarted or reconfigured.
 11. The system of claim 1, wherein theexecution history of the particular software program includes at leastpart of a log file generated by the particular software program.
 12. Thesystem of claim 1, wherein the predictive model was either trained onthe corpus of execution histories using unsupervised machine learning,or trained on labelled entries of the corpus of execution historiesusing supervised machine learning.
 13. The system of claim 1, whereinthe predictive model and the automated repair controller application aredisposed within a computational instance of a remote network managementplatform that is configured to manage the network, and wherein receivingthe representation of the execution history and causing the repairapplication to be executed within the network occur by way of a proxyserver disposed within the network.
 14. A computer-implemented methodcomprising: receiving, by a predictive model, a representation of anexecution history of a particular software program, wherein thepredictive model has been trained on a corpus of execution histories ofsoftware programs in order to be able to estimate root causes ofsoftware program failures, wherein persistent storage contains rulesrelated to repairing software programs in a network, and wherein therules associate indications of software program failures with repairapplications implemented by an automated repair controller according todomain knowledge of the network, wherein the repair applications areconfigured to correct corresponding software program failures;generating, by the predictive model and from the execution history, afailure prediction for the particular software program, the failureprediction including an estimated root cause; receiving, by an automatedrepair controller application, the failure prediction from thepredictive model; based on applying the rules to the failure prediction,determining, by the automated repair controller application, a repairapplication from the repair applications that is configured to correctthe estimated root cause; and causing, by the automated repaircontroller application, the repair application to be executed within thenetwork.
 15. The computer-implemented method of claim 14, wherein therules each include: (i) a repair application reference, and (ii) one ormore of a software program name, a software program failure indication,or a network address.
 16. The computer-implemented method of claim 15,wherein the failure prediction includes at least one of the softwareprogram name the software program failure indication, or the networkaddress.
 17. The computer-implemented method of claim 16, whereindetermining the repair application that is configured to correct theestimated root cause comprises: selecting a particular rule from therules, wherein the particular rule matches one or more of the softwareprogram name, the software program failure indication, or the networkaddress included in the failure prediction, and wherein the particularrule contains a particular repair application reference to the repairapplication.
 18. The computer-implemented method of claim 15, whereinthe network address is of a computing device on which the particularsoftware program executes or with which the particular software programattempts to communicate.
 19. The computer-implemented method of claim14, wherein execution of the repair application causes: the particularsoftware program to be restarted or reconfigured, or an application,device, or service used by the particular software program to berestarted or reconfigured.
 20. An article of manufacture including anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations comprising: receiving, by apredictive model, a representation of an execution history of aparticular software program, wherein the predictive model has beentrained on a corpus of execution histories of software programs in orderto be able to estimate root causes of software program failures, whereinpersistent storage contains rules related to repairing software programsin a network, and wherein the rules associate indications of softwareprogram failures with repair applications implemented by an automatedrepair controller according to domain knowledge of the network, whereinthe repair applications are configured to correct corresponding softwareprogram failures; generating, by the predictive model and from theexecution history, a failure prediction for the particular softwareprogram, the failure prediction including an estimated root cause;receiving, by an automated repair controller application, the failureprediction from the predictive model; based on applying the rules to thefailure prediction, determining, by the automated repair controllerapplication, a repair application from the repair applications that isconfigured to correct the estimated root cause; and causing, by theautomated repair controller application, the repair application to beexecuted within the network.